Cover Image

DOGE-Train: Discrete Optimization on GPU with End-to-end Training

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Abbas, Ahmed, Swoboda, Paul
Format:	Preprint
Published:	2022
Subjects:	Machine Learning Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Sobolev Training of End-to-End Optimization Proxies
by: Rosemberg, Andrew W., et al.
Published: (2025)

Training Neural ODEs Using Fully Discretized Simultaneous Optimization
by: Shapovalova, Mariia, et al.
Published: (2025)

End-to-End Training of High-Dimensional Optimal Control with Implicit Hamiltonians via Jacobian-Free Backpropagation
by: Gelphman, Eric, et al.
Published: (2025)

End-to-End Conformal Calibration for Optimization Under Uncertainty
by: Yeh, Christopher, et al.
Published: (2024)

Optimization Over Trained Neural Networks: Taking a Relaxing Walk
by: Tong, Jiatai, et al.
Published: (2024)

Towards Guided Descent: Optimization Algorithms for Training Neural Networks At Scale
by: Nagwekar, Ansh
Published: (2025)

Neural Network Training Techniques Regularize Optimization Trajectory: An Empirical Study
by: Chen, Cheng, et al.
Published: (2020)

End-to-end guarantees for indirect data-driven control of bilinear systems with finite stochastic data
by: Chatzikiriakos, Nicolas, et al.
Published: (2024)

Chebyshev Center-Based Direction Selection for Multi-Objective Optimization and Training PINNs
by: Yoon, Hoyeol, et al.
Published: (2026)

TAMUNA: Doubly Accelerated Distributed Optimization with Local Training, Compression, and Partial Participation
by: Condat, Laurent, et al.
Published: (2023)

Mean-Field Limits for Two-Layer Neural Networks Trained with Consensus-Based Optimization
by: De Deyn, William, et al.
Published: (2025)

BiCoLoR: Communication-Efficient Optimization with Bidirectional Compression and Local Training
by: Condat, Laurent, et al.
Published: (2026)

Optimization over Trained (and Sparse) Neural Networks: A Surrogate within a Surrogate
by: Pham, Hung, et al.
Published: (2025)

The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training
by: Schaipp, Fabian, et al.
Published: (2025)

Adversarial Training of Two-Layer Polynomial and ReLU Activation Networks via Convex Optimization
by: Kuelbs, Daniel, et al.
Published: (2024)

Convergence for Discrete Parameter Update Schemes
by: Wilson, Paul, et al.
Published: (2025)

Reheated Gradient-based Discrete Sampling for Combinatorial Optimization
by: Li, Muheng, et al.
Published: (2025)

End-to-End Learning Framework for Solving Non-Markovian Optimal Control
by: Zhang, Xiaole, et al.
Published: (2025)

Anytime Training with Schedule-Free Spectral Optimization
by: Apte, Anuj, et al.
Published: (2026)

Efficient Optimization Algorithms for Linear Adversarial Training
by: RIbeiro, Antônio H., et al.
Published: (2024)

Beyond Discretization: Learning the Optimal Solution Path
by: Dong, Qiran, et al.
Published: (2024)

$ϕ$-Balancing for Mixture-of-Experts Training
by: Chen, Lizhang, et al.
Published: (2026)

An Optimal Control Approach To Transformer Training
by: Akman, Kağan, et al.
Published: (2026)

Does Weight Decay Enhance Training Stability?
by: Saether, Marius, et al.
Published: (2026)

Distributional Adversarial Attacks and Training in Deep Hedging
by: He, Guangyi, et al.
Published: (2025)

Causal-Aware Foundation-Model for Bilevel Optimization in Discrete Choice Settings
by: Subramanian, Shivaram, et al.
Published: (2026)

SPO-VCS: An End-to-End Smart Predict-then-Optimize Framework with Alternating Differentiation Method for Relocation Problems in Large-Scale Vehicle Crowd Sensing
by: Wang, Xinyu, et al.
Published: (2024)

Robust and Fast Training via Per-Sample Clipping
by: Nobile, Davide, et al.
Published: (2026)

Relaxation-Informed Training of Neural Network Surrogate Models
by: Tsay, Calvin
Published: (2026)

Challenges in Training PINNs: A Loss Landscape Perspective
by: Rathore, Pratik, et al.
Published: (2024)

Adaptive Momentum and Nonlinear Damping for Neural Network Training
by: Karoni, Aikaterini, et al.
Published: (2026)

Stable Nonconvex-Nonconcave Training via Linear Interpolation
by: Pethick, Thomas, et al.
Published: (2023)

Training Deep Learning Models with Norm-Constrained LMOs
by: Pethick, Thomas, et al.
Published: (2025)

Enhancing Privacy in Federated Learning through Local Training
by: Bastianello, Nicola, et al.
Published: (2024)

Tuning-Free Stochastic Optimization
by: Khaled, Ahmed, et al.
Published: (2024)

On Dissipativity of Cross-Entropy Loss in Training ResNets
by: Püttschneider, Jens, et al.
Published: (2024)

Towards an Optimal Control Perspective of ResNet Training
by: Püttschneider, Jens, et al.
Published: (2025)

Employing Federated Learning for Training Autonomous HVAC Systems
by: Hagström, Fredrik, et al.
Published: (2024)

Compression-aware Training of Neural Networks using Frank-Wolfe
by: Zimmer, Max, et al.
Published: (2022)

TwIST: Rigging the Lottery in Transformers with Independent Subnetwork Training
by: Menezes, Michael, et al.
Published: (2025)